AMEF‐Net: Towards an attention and multi‐level enhancement fusion for medical image classification in Parkinson's aided diagnosis

Abstract

Parkinson's disease (PD) is a neurodegenerative disorder primarily affecting middle‐aged and elderly populations. Its insidious onset, high disability rate, long diagnostic cycle, and high diagnostic costs impose a heavy burden on patients and their families. Leveraging artificial intelligence, with its rapid diagnostic speed, high accuracy, and fatigue resistance, to achieve intelligent assisted diagnosis of PD holds significant promise for alleviating patients' financial stress, reducing diagnostic cycles, and helping patients seize the golden period for early treatment. This paper proposes an Attention and Multi‐level Enhancement Fusion Network (AMEF‐Net) based on the characteristics of three‐dimensional medical imaging and the specific manifestations of PD in medical images. The focus is on small lesion areas and structural lesion areas that are often overlooked in traditional deep learning models, achieving multi‐level attention and processing of imaging information. The model achieved a diagnostic accuracy of 98.867%, a precision of 99.830%, a sensitivity of 99.182%, and a specificity of 99.384% on Magnetic Resonance Images from the Parkinson's Progression Markers Initiative dataset. On Diffusion Tensor Images, it achieved a diagnostic accuracy of 99.602%, a precision of 99.930%, a sensitivity of 99.463%, and a specificity of 99.877%. The relevant code has been placed in https://github.com/EdwardTj/AMEF‐NET.